A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

Gao, Li; Gan, Yi; Shi, Jun-cheng

doi:10.1007/s10489-022-03182-3

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…After calculating the mean value of the first-order IMF, CEEMDAN adds Gaussian white noise to the residual IMF again, iteratively until the end. The detailed process is as follows [30,31]:…”

Section: Ceemdan Principlementioning

confidence: 99%

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

Meng,

Cai,

et al. 2023

Electronics

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Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of physiological electrical signals in fish is more susceptible than in mammals to the effects of surface mucus and water noise, thereby reducing signal quality. In this study, a noise reduction method for electrophysiological behavioral signals in fish was proposed, namely the decomposition of the original EMG signal into multiple intrinsic mode components using CEEMDAN. To choose the signal-dominated IMF, noise-dominated IMF, and pure IMF, mutual correlation function characteristic analysis is done on each IMF and the original signal. The signal-dominated IMF is then filtered using the improved wavelet thresholding approach. Finally, the wavelet threshold filtered signal-dominated IMF with pure IMF was reconstructed into the processed fish EMG signal. It is demonstrated that the algorithm proposed in this paper improves the SNR by 3.1977 dB and reduces the RMSE by 0.0235 when compared to the traditional wavelet threshold denoising. The denoising method proposed in this paper can effectively improve the signal quality and provides an effective tool for the in-depth analysis of fish behavior from the perspective of physiological electrical signals.

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Section: Ceemdan Principlementioning

confidence: 99%

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

Meng,

Cai,

et al. 2023

Electronics

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show abstract

“…小波变换是一种多尺度信号分析方法, 为信号去噪提供了强有力的工具. 观测信号经小波变换后, 噪声成分的能量主要集中在高频部分, 且比较均匀地分布在个数较多但幅值较小的小波系数上, 而有效信号成分的能量则集中分布在少数几个幅值较大的小波系数上 [13] . 信号的小波系数值大于噪声的小波系数值.…”

Section: 小波阈值去噪原理 X(t)unclassified

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

Fan,

Li,

Yang

et al. 2023

Acta Phys. Sin.

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The echo of underwater lidar often contains a significant amount of scattering clutters. In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar, a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising is proposed.<br/>The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select Intrinsic Mode Function(IMF) components obtained from the CEEMDAN decomposition. IMFs which are more closely correlated to the original signal are selected. Then, the wavelet thresholding denoising algorithm is applied to each of the selected IMF to perform additional denoising. For each IMF component, specific threshold values are calculated based on their frequency and amplitude characteristics. Subsequently, the wavelet coefficients of the IMF components are processed using these threshold values. Finally, the denoised IMF components are combined and reconstructed to obtain the final denoised signal. Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods. By using threshold values calculated based on the characteristics of each IMF component, the wavelet thresholding denoising process has been improved compared to directly using a single threshold value. This approach enhances the algorithm's adaptability and enables more effective removal of noise from the signal.<br/>We applied the proposed method to underwater ranging experiments. A 532 nm intensity-modulated continuous wave laser was used as the light source. Ranging was performed for a target in a water with varying attenuation coefficients. A white polyvinyl chloride (PVC) reflector was used as the target. When directly using correlation extreme values to determine the delay at a distance of 3.75 attenuation lengths, it resulted in a ranging error of 19.2 cm. However, after applying the proposed method, the ranging error was reduced to 6.2 cm, the ranging accuracy was effectively improved. These results demonstrate that the method has a significant denoising effect in underwater lidar system.

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A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

Wang

2023

Appl Intell

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A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

Cited by 11 publications

References 28 publications

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

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